Currently, vision-based sensors are attracting more and more attentions. From the perspective of environmental perception, the vision-based sensors have advantages such as more and richer available information, short sampling period, less interference from a magnetic field and other sensors, low weight, small energy consumption, low cost and convenient usage, and thus the vision-based sensors play a more and more important role in active safety of urban roads. Vision-based active protection technologies for vulnerable road users such as passerby have been a research hotspot at home and abroad.
A generalized passerby may refer to a vulnerable road user such as a walking pedestrian, a bicycle rider or a motorcycle rider. An existing research on the vision-based active protection technologies for vulnerable road users such as passerby is mainly limited to passerby detection in the usual sense, i.e., detection for the walking pedestrian, and there are few researches on the vulnerable generalized passerby of the road users such as the bicycle rider or the motorcycle rider.
In the traditional technology, the bicycle rider and the motorcycle rider are treated as objects different from the pedestrian. Samples of the bicycle rider and samples of the motorcycle rider are collected respectively. Features of the bicycle rider and features of the motorcycle rider are extracted from a preset rider sample database, and a classifier for the bicycle rider and a classifier for the motorcycle rider are generated by training with the extracted features. An input image is detected by the classifiers, to determine whether a bicycle rider or a motorcycle rider exists in the input image.
In the above solution, it takes a lot of time to collect the samples, extract the features and train the classifiers, which results in a time-consuming and low-efficiency detection.